AC ekyc-suite-media-labeling
eKYC Suite Media Labeling is the focused ClawHub KYC media labeling Skill, KYC image labeling Skill, and onboarding media-review Skill under the eKYC Suite brand. Use it when an AI agent must review selected consented image or video labels during KYC onboarding, including multiple people, face covering, coercion indicators, phone use, hats, sunglasses, vehicle scenes, hotel scenes, or other supported media-risk signals. It returns structured label results from the configured eKYC Suite Cloud backend. Do not use it for unrestricted image captioning, face comparison, document OCR, conceptual KYC questions, or final high-impact decisions without human review.
eKYC Suite Media Labeling is the focused ClawHub KYC media labeling Skill, KYC image labeling Skill, and onboarding media-review Skill under the eKYC Suite…
As a process C 60/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
How to improve
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "env" - note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 60/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 9 mutating operations with no state check
- 100Tools and files. No external tools needed
- 100Steps. 13 steps
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1266 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 10 top-level sections: this looks like several domains in one skill
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 664: enough signal without eating the budget
- +4Structure: 11 headings
- +3Step-by-step instructions: 13 items
- +4Has examples (1 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.